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Background: Asymmetry analysis of retinal layers in right and left eyes can be a valuable tool for early diagnoses of retinal diseases. To determine the limits of the normal interocular asymmetry in retinal layers around macula, thickness measurements are obtained with optical coherence tomography (OCT)., Methods: For this purpose, after segmentation of intraretinal layer in threedimensional OCT data and calculating the midmacular point, the TM of each layer is obtained in 9 sectors in concentric circles around the macula. To compare corresponding sectors in the right and left eyes, the TMs of the left and right images are registered by alignment of retinal raphe (i.e. diskfovea axes). Since the retinal raphe of macular OCTs is not calculable due to limited region size, the TMs are registered by first aligning corresponding retinal raphe of fundus images and then registration of the OCTs to aligned fundus images. To analyze the asymmetry in each retinal layer, the mean and standard deviation of thickness in 9 sectors of 11 layers are calculated in 50 normal individuals., Results: The results demonstrate that some sectors of retinal layers have signifcant asymmetry with P < 0.05 in normal population. In this base, the tolerance limits for normal individuals are calculated., Conclusion: This article shows that normal population does not have identical retinal information in both eyes, and without considering this reality, normal asymmetry in information gathered from both eyes might be interpreted as retinal disorders., Competing Interests: There are no conflicts of interest., (Copyright: © 2021 Journal of Medical Signals & Sensors.)

Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and diseases such as diabetic, hypertension, stroke, and the other cardiovascular diseases in adults as well as retinopathy of prematurity in infants. Retinal fundus images provide non-invasive visualization of the retinal vessel structure. Applying image processing techniques in the study of digital color fundus photographs and analyzing their vasculature is a reliable approach for early diagnosis of the aforementioned diseases. Reduction in the arteriolar-venular ratio of retina is one of the primary signs of hypertension, diabetic, and cardiovascular diseases which can be calculated by analyzing the fundus images. To achieve a precise measuring of this parameter and meaningful diagnostic results, accurate classification of arteries and veins is necessary. Classification of vessels in fundus images faces with some challenges that make it difficult. In this paper, a comprehensive study of the proposed methods for classification of arteries and veins in fundus images is presented. Considering that these methods are evaluated on different datasets and use different evaluation criteria, it is not possible to conduct a fair comparison of their performance. Therefore, we evaluate the classification methods from modeling perspective. This analysis reveals that most of the proposed approaches have focused on statistics, and geometric models in spatial domain and transform domain models have received less attention. This could suggest the possibility of using transform models, especially data adaptive ones, for modeling of the fundus images in future classification approaches., Competing Interests: There are no conflicts of interest.

Diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. Thus, the need for precise segmentation of corneal layer boundaries is inevitable. Obviously, manual segmentation is time-consuming and imprecise. In this paper, the Gaussian mixture model (GMM) is used for automatic segmentation of three clinically important corneal boundaries on optical coherence tomography (OCT) images. For this purpose, we apply the GMM method in two consequent steps. In the first step, the GMM is applied on the original image to localize the first and the last boundaries. In the next step, gradient response of a contrast enhanced version of the image is fed into another GMM algorithm to obtain a more clear result around the second boundary. Finally, the first boundary is traced toward down to localize the exact location of the second boundary. We tested the performance of the algorithm on images taken from a Heidelberg OCT imaging system. To evaluate our approach, the automatic boundary results are compared with the boundaries that have been segmented manually by two corneal specialists. The quantitative results show that the proposed method segments the desired boundaries with a great accuracy. Unsigned mean errors between the results of the proposed method and the manual segmentation are 0.332, 0.421, and 0.795 for detection of epithelium, Bowman, and endothelium boundaries, respectively. Unsigned mean errors of the inter-observer between two corneal specialists have also a comparable unsigned value of 0.330, 0.398, and 0.534, respectively.

Optical coherence tomography (OCT) is a recently established imaging technique to describe different information about the internal structures of an object and to image various aspects of biological tissues. OCT image segmentation is mostly introduced on retinal OCT to localize the intra-retinal boundaries. Here, we review some of the important image segmentation methods for processing retinal OCT images. We may classify the OCT segmentation approaches into five distinct groups according to the image domain subjected to the segmentation algorithm. Current researches in OCT segmentation are mostly based on improving the accuracy and precision, and on reducing the required processing time. There is no doubt that current 3-D imaging modalities are now moving the research projects toward volume segmentation along with 3-D rendering and visualization. It is also important to develop robust methods capable of dealing with pathologic cases in OCT imaging.

In this paper, we try to find a particular combination of wavelet shrinkage and nonlinear diffusion for noise removal in dental images. We selected the wavelet diffusion and modified its automatic threshold selection by proposing new models for speckle-related modulus. The Laplacian mixture model, Rayleigh mixture model, and circular symmetric Laplacian mixture models were evaluated and, as it could be expected, the latter provided a better model because of its compatibility with heavy tailed structure of the wavelet coefficients besides their interscale dependence. The numerical evaluation of contrast-to-noise ratio (CNR) along with simple observation of the results showed reasonably acceptable improvement of CNR from 2.9149 to 38.8813 in anterior--posterior images, from 41.6131 to 86.3141 in cephal-lateral images, from 13.6414 to 43.4711 in intraoral pictures, and from 6.0102 to 31.8771 in panoramic datasets. Furthermore, technical ability of the proposed filtering method in retaining the possible cavities on dental images was evaluated in two datasets with natural and artificially applied cavities.